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In January 2022, the unemployment rate for all German residents was 6,3 percent. For non-German citizens, it was 13,1 percent.
Motivating question: could these gaps in employment be due to (illegal) hiring discrimination? Or might they be due to something else?
An example of traditional racism can be measured in LaPiere’s 1930 study. When asked “Will you accept members of the Chinese race as guests in your establishment?”, more than 90 percent of the proprietors indicated unequivocal refusal.
Thankfully, much has changed since LaPiere’s time. To quote from Pager and Weston:
“Many individuals in contemporary society experience few conscious anti-Black sentiments, and traditional measures of prejudice have substantially declined. At the same time, there remains a high level of generalized anxiety or discomfort with Blacks that can [unconsciously] shape interracial interaction and decision-making…Aversive racists believe in equality and consciously eschew distinctions on the basis of race; unconscious bias, however, leads to situations in which subtle forms of discrimination persist without the actor’s awareness.”
Word, Zanna, and Cooper (1974) show that white subjects conducting mock interviews with trained black applicants make more speech errors, ask fewer questions, and terminate interviews more quickly than with similar white applicants.
How might this distinction between (overt and conscious) “traditional” racism and (subtle and unconscious) “aversive” racism explain the discrepancy between what employers say and what they do?
Taste-based discrimination arises when individuals harbour an idiosyncratic preference against interacting with minorities. It is “irrational” in that the employer has no reason to discriminate against the minority candidate, except that he simply dislikes minorities.
In contrast, statistical discrimination emphasizes the ways in which discrimination is rationally-motivated, and focuses on the incomplete information problem facing potential employers.
For instance, employers lacking information on the productivity or “soft skills” of job applicants may use group-level estimates contained in ethnic or racial stereotypes as a screening tool in their hiring decisions.
Importantly, discriminatory behaviour in such models is not driven by a biased response to race or ethnicity per se, but rather by a lack of information about an individual’s true characteristics. In other words, if employers could observe the “true” productivity of every individual applicant, then – under this reasoning – employers would never “need” to discriminate.
Imagine we conduct a correspondence study on housing discrimination.
Suppose that landlords are only concerned about
tenants’ ability to pay the rent. Suppose further that minority renters
really are, on average, poorer than majority renters.
We run an experiment where we send a short email to landlords:
Hello,
I saw your flat listed on rentalflats.com and I would like to com by for a showing. Please get in touch with me.
Regards, [NAME]
We have 4 signatures, randomly assigned:
Suppose we find discrimination against Mohammed el-Fatih (vs. Michael Fischer), but no discrimination against Prof. Dr. Mohammed el-Fatih (vs. Prof. Dr. Michael Fischer).
What would you infer from this result about why employers discriminate?
Suppose instead we find discrimination against Mohammed el-Fatih (vs. Michael Fischer), and the same amount of discrimination against Prof. Dr. Mohammed el-Fatih (vs. Prof. Dr. Michael Fischer).
What would you now infer about why employers discriminate?
Split up into groups:
In your groups:
BTW: one argument for why correspondence tests / audits generally find low employment discrimination in Germany (compared to other countries) is that German job applications are very detailed. Thus there may be less room for employers to statistically discriminate.
Koopmans et al. adopt a different approach to disentangling taste-based vs. statistical discrimination.
They first measure the average educational level (education = productivity) and the average cultural values (greater value distance = “distaste”) for a wide range of groups in Germany.
They then conduct a correspondence test:
Finally, they conduct a version of the following regression analysis:
(1) Callback = EthnicName (2) Callback = EthnicName + education + values
And ask whether the inclusion of measures for education and / or values makes the signifiance of EthnicName go away.
Questions